Contamination accumulation modeling

ABSTRACT

A wash optimization system and related methods are provided that increase the efficiency and the effectiveness of engine washes. A system comprising at least one processor receives sensor data representing one or more measured parameters of a turbine engine and determines at least one performance parameter based on the sensor data. The at least one performance parameter represents one or more particulate values associated with the turbine engine. The system generates a health state for the turbine engine based on the at least one performance parameter and generates a wash identifier based on the health state of the turbine engine.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/045,049, filed Jul. 25, 2018, which claims the benefit ofpriority of U.S. Provisional Patent Application No. 62/562,050,“CONTAMINATION ACCUMULATION MODELING,” filed Sep. 22, 2017, the contentsof which are incorporated herein by reference in their entirety as ifset forth verbatim.

FIELD

The present disclosure relates generally to jet engine maintenance.

BACKGROUND

An aerial vehicle can rely on one or more jet engines to control theaerial vehicle. Engine performance can be affected by cleanliness of theengine. Washing the engine regularly can improve the performance of theengine and extend the life of the engine. However, different types ofengine washes can have different costs and different levels ofeffectiveness. It can be difficult to determine an appropriate enginewash.

BRIEF DESCRIPTION

Aspects and advantages of the disclosed technology will be set forth inpart in the following description, or may be obvious from thedescription, or may be learned through practice of the invention.

According to example aspects of the disclosed technology, there isprovided a method, comprising: receiving, by a system comprising atleast one processor, sensor data representing one or more measuredengine parameters of a turbine engine; determining, by the system, atleast one performance parameter based on the sensor data, the at leastone performance parameter representing one or more particulate valuesassociated with the turbine engine; generating, by the system, a healthstate associated with the turbine engine using the one or moreparticulate values; and generating, by the system, a wash identifierbased on the health state of the turbine engine.

According to example aspects of the disclosed technology, there isprovided a computing device, comprising: one or more storage devicescomprising processor readable code; and one or more processors incommunication with the one or more storage devices, the one or moreprocessors execute the processor readable code to: receive a first setof one or more performance parameters based on engine sensor data priorto at least one wash event and a second set of one or more performanceparameters based on engine sensor data subsequent to the at least onewash event, the first and second sets of performance parametersrepresenting one or more particulate values associated with the turbineengine; determine a recovery parameter based on a difference between thefirst set of performance parameters and the second set of performanceparameters; generate a contamination accumulation model based on therecovery parameter; and generate a wash identifier based on thecontamination accumulation model.

According to example aspects of the disclosed technology, there isprovided a non-transitory computer-readable medium storing computerinstructions, that when executed by one or more processors, cause theone or more processors to perform the steps of: receiving, by a systemcomprising at least one processor, sensor data representing one or moremeasured engine parameters of a turbine engine; determining, by thesystem, one or more particulate values based on the sensor data;determining, by the system, a health state associated with the turbineengine using the one or more particulate values; determining, by thesystem, that the health state satisfies a threshold; and generating, bythe system, a wash advisory output in response to determining that thehealth state satisfies a threshold.

These and other features, aspects and advantages of the disclosedtechnology will become better understood with reference to the followingdescription and appended claims. The accompanying drawings, which areincorporated in and constitute a part of this specification, illustrateembodiments of the disclosed technology and, together with thedescription, serve to explain the principles of the disclosedtechnology.

BRIEF DESCRIPTION OF THE DRAWINGS

A full and enabling disclosure of the present disclosure, including thebest mode thereof, directed to one of ordinary skill in the art, is setforth in the specification, which makes reference to the appendedfigures, in which:

FIG. 1 depicts an aerial vehicle in accordance with example embodiments.

FIG. 2 is a block diagram of a wash optimization system in accordancewith example embodiments.

FIG. 3 is a flowchart describing example embodiments of a process foroptimizing wash events for a turbine engine.

FIG. 4 is a block diagram of a wash optimization system in accordancewith example embodiments.

FIG. 5 is a flowchart describing example embodiments of a process forgenerating a contamination accumulation model.

FIG. 6 is a flowchart describing example embodiments of a process forgenerating a wash identifier.

FIG. 7 is flowchart describing example embodiments of a process fordetermining a wash type and wash timing.

FIG. 8 is a flowchart describing example embodiments of a process fordetermining a wash scope.

FIG. 9 is a perspective view of a wash module in accordance with exampleembodiments.

FIG. 10 is a schematic view of a wash module in accordance with exampleembodiments.

FIG. 11 is a schematic view of a distribution manifold in accordancewith example embodiments, as may be incorporated in the wash module ofFIG. 9.

FIG. 12 is a schematic view of a wash module in accordance with exampleembodiments, operable with a gas turbine engine.

FIG. 13 is a block diagram depicting a wash system including a wand inaccordance with example embodiments.

FIG. 14 is a block diagram of an example of a computing system.

DETAILED DESCRIPTION

Reference now will be made in detail to embodiments of the disclosure,one or more examples of which are illustrated in the drawings. Eachexample is provided by way of explanation, not limitation of thedisclosed embodiments. In fact, it will be apparent to those skilled inthe art that various modifications and variations can be made in thepresent disclosure without departing from the scope or spirit of theclaims. For instance, features illustrated or described as part ofexample embodiments can be used with another embodiment to yield a stillfurther embodiment. Thus, it is intended that the present disclosurecovers such modifications and variations as come within the scope of theappended claims and their equivalents.

Example aspects of the present disclosure are directed to systems andmethods for jet engine maintenance, and more particularly, to techniquesfor optimizing cleaning of a jet engine. The accumulation ofcontaminants within jet engine components such as the compressor andturbine result in deterioration in engine performance over time. Thecontaminants restrict air flow through the engine, resulting in a lossof engine efficiency as more fuel is burned to maintain a desired levelof engine performance. Washing an engine can remove engine contaminantsand reduce performance deteriorations. Traditionally engine maintenanceincluding washing are performed according to predefined maintenanceschedules. These maintenance schedules do not typically consider theactual performance and/or condition of an engine in making washdecisions. Instead, traditional techniques rely on simple operationaldata such as flight time to determine when to wash an engine. Theseschedules do not consider how a particular engine may be affected bydifferent types, durations, timings, and delivery methods of an enginewash.

According to example embodiments of the disclosed technology, a systemand method of jet engine maintenance are provided that utilizecontamination accumulation modeling to predict or project particulateaccumulations within a turbine engine. The system can assess therecovery associated with wash events to develop a contaminantaccumulation model. The system can then use the model to determineparticulate accumulations based on sensor data. Based on particulatecalculations, the system can optimize wash timings to maximize hardwarelife.

In some embodiments, particulate accumulation information is used todetermine a health state associated with an engine based on contaminateaccumulation modeling. Using the performance parameters that representcontamination, the system optimizes washing for the engine. For example,the system can compare a measured health state of an engine or componentto a reference state. If a difference between the measured health stateand reference state satisfies a threshold, the system can generate awash identifier, such as by generating and transmitting a signalincluding the wash identifier. The wash identifier can trigger a washevent. In some embodiments, the wash identifier identifies the washevent, and optionally indicates a wash type, scope, duration, timing,etc.

In some embodiments, the system receives sensor data that representsmeasured engine parameters. The system determines performance parametersthat represent a measured condition and/or performance of the engine. Insome embodiments, the performance parameters include a particulateparameter that represents contamination within the engine. The systemcan update performance parameters automatically and over time asadditional sensor data is received. Based on the particulateaccumulations as represented by the performance parameters, the systemgenerates a health state for the engine and/or one or more components ofthe engine. The system can generate historic, current, and/or apredicted health state of the engine. Using the health stateinformation, the system generates wash identifiers to optimize cleaningof the engine and therefore, maximize the useful life of the engineand/or its components. In some embodiments, the health state comprisesone or more performance parameters.

The system may use additional performance parameters as part ofgenerating wash identifiers based on measured condition and/orperformance. In some embodiments, the system determines a compressorefficiency parameter, such as a value indicating a current or predictedefficiency of the compressor. In some embodiments, the system determinesa compressor performance parameter. Similarly, the system can determinea turbine efficiency and/or performance parameter.

In some embodiments, the system generates a wash identifier indicating awash type, wash timing, and/or wash scope. A wash type can include oneor more of a wash medium, wash duration, and wash delivery method orsystem. By way of example, the wash identifier may identify an externalwand delivery of water for a predetermined duration or an wash lineinternal delivery method of a foam cleaning medium be used for anotherpredetermined duration.

A wash timing identifier can identify a particular timing for orinterval during which to perform the engine wash. The system canoptimize the timing to extend hardware life and efficiently maintainengines without unnecessary cost.

A wash scope identifier identifies a scope of the wash, including athoroughness and/or identification of particular engine or componentlocations to wash. The wash scope identifier can identify components ofthe delivery system to use such as valves or wash lines in order totarget particular portions of an engine.

An improved system for automating wash determinations for jet enginemaintenance is provided. A processor-based system is configured as aspecialized device that provides efficient and accurate determinationsfor an engine wash based on sensor data representing measured engineparameters. The automated conversion of sensor data into performanceparameters and a health state, and subsequent comparison with referencestate information more accurately determines when an engine wash shouldbe performed, how it should be performed, and to what areas the washshould be applied. An improved computational performance is achieved,while providing increased turbine engine performance through efficientand automated maintenance scheduling.

FIG. 1 depicts an example aerial vehicle 200 in accordance with exampleembodiments of the present disclosure. The aerial vehicle 200 caninclude one or more engines 202, one or more sensors 204, a computingsystem 206, and a communication bus 208 to connect at least one of theone or more sensors 204 with the computing system 206. The one or moresensors 104 can detect one or more parameters related to engineperformance, such as Exhaust Gas Temperature (EGT), EGT Hot Day Margin(EGTHDM), fuel burn, modular efficiency, other analytic measures ofengine performance, the like, and/or any combination of the foregoing.The one or more sensors 204 can communicate the one or more detectedparameters to the computing system 206 via the communication bus 208.The computing system 206 can be, for example, the computing system 600described in more detail in FIG. 14. The computing system 206 cantransmit the detected one or more parameters to a computing systemassociated with a ground system.

The numbers, locations, and/or orientations of the components of exampleaerial vehicle 200 are for purposes of illustration and discussion andare not intended to be limiting. Those of ordinary skill in the art,using the disclosures provided herein, shall understand that thenumbers, locations, and/or orientations of the components of the aerialvehicle 200 can be adjusted without deviating from the scope of thepresent disclosure.

FIG. 2 is a block diagram of a wash optimization system 300 according toexample embodiments of the disclosed technology. In one example, system300 is implemented using one or more computing devices, such as one ormore servers, client devices, and/or network devices. The one or morecomputing systems are one or more computing systems 600 in one example.Wash optimization system 300 may be implemented in a control systemaboard an aerial vehicle, such as by computing system 206, or can beimplemented as a ground-based system that receives sensor and/or otherinformation from an aerial vehicle.

Increased efficiency may result from increased airflow within the engineas a result of washing. Different types of engine washes may havedifferent effectiveness levels in reducing engine deterioration. Awater-wash in often applied to a turbine engine to remove contaminants.Flushing the engine compressors may remove dirt, sand, and/or othercontaminants that accumulate over time. Other cleaning mediums such as awater and detergent combination, water and Isopropyl alcoholcombination, foaming solution, and the like can also be used. An enginewash can be applied for different durations and with various deliverymethods. For example, a wand can be positioned externally adjacent tothe engine to deliver a cleaning medium to the engine. Alternatively,wash lines can be used that are coupled to one or more portions of theengine to directly provide the cleaning medium to or within the engine.FIG. 2 describes a system that optimizes washing of an engine based oncondition and/or performance information of the engine.

Wash optimization system 300 includes a gas port tracking filter 304,high-pressure turbine (HPT) remaining useful life (RUL) component 306,high-pressure performance component 308, compressor efficiency component310, cumulative damage component 312, health state component 314, washinterval component 316, wash type component 318, wash scope component320, HPT clean component 322, HTP clean component 324, and washeffectiveness component 326. The components of system 300 may beconfigured at a single computing device, or may be distributed acrossmultiple computing devices. Each of the various components may beimplemented with hardware, software, or a combination of both hardwareand software as hereinafter described.

In general, GPTF 304, HPT RUL component 306, and HPC performancecomponent 308 are configured to receive sensor data 302 from one or moresensors of a turbine engine. The sensor data represents one or moreengine parameters measured from the jet engine. Various sensor data 302may be used with embodiments of the disclosed technology. By way ofexample and not limitation, sensor data 302 may include environmentaldata and/or operational data. In one example, environmental data mayinclude ambient pressures, temperatures, altitudes, humidity, latitude,longitude, flight city, and/or particulates. Particulates can includeany type of particulate such as multiple dust/sand types,pollution/haze, sea salt, smoke, volcanic ash and sulfates. In oneexample, operational data may include engine parameters such as gasturbine temperatures, pressures, fuel flow, rotor speeds, bleeds(alltypes), horsepower extraction, length of flight, and/or derivedparameters based on operational conditions.

Although not shown, components 304, 306, and 308 may receive and/orgenerate additional data such as product parameters. Product parametersmay include modular health state, accumulated conditions in parts whichmay be related to environmental factors, and/or derived damage factorsbased on models or analytics. Some other examples include modularcomponent efficiencies, flows and pressure ratios.

GPTF 304 receives sensor data 302 to track and analyze the sensor dataover time. The GPTF can be used to determine the effects of componentdeterioration, sensor biases, engine to engine variations, etc. Thetracking filter can be used to adjust model outputs to match sensoroutputs. For example, sensor data including analog signals such ascurrent, resistance, or voltage may be received from a sensor. Forexample, the turbine engine sensor can measure rotor speed, temperature,pressure, etc. The tracking filter can covert or scale sensor data toprovide a sensor output in a standard units form such as in units ofpsi, rpm, etc.

In some embodiments, GPTF 304 is configured to compare calculated engineparameter values with expected engine parameters. Expected engineparameters can be determined using a reference state as hereinafterdescribed. Expected engine parameters may be defined by an engine model.GPTF 304 may generate performance modifiers in some embodiments thatindicate a difference between expected and calculated engine parameters.The calculated engine parameters and/or performance modifiers areprovided to compressor efficiency component 310.

Compressor efficiency component 310 is configured to calculate acompressor efficiency parameter in some embodiments. The efficiencyparameter is calculated based on the calculated engine parameter and/orperformance modifiers. The compressor efficiency parameter is oneexample of a performance parameter representing a measured performanceof the turbine engine. The efficiency parameter can represent low orhigh pressure compressor component efficiency. The efficiency parametercan include cooling flows, bleeds, pressure losses, etc. In exampleembodiments, the compressor efficiency component 310 can generate acompressor performance parameter of the turbine engine based on sensordata. Component 310 can generate a compressor efficiency parameter ofthe turbine engine based on the sensor data. A health state of an enginecan be determined by comparing the compressor performance parameter toan expected compressor performance parameter and comparing thecompressor efficiency parameter to an expected compressor performanceparameter. In some examples, generating a compressor efficiencyparameter comprises applying a gas port tracking filter to the sensordata.

HPC performance component 308 is configured to receive sensor data 302and calculate one or more performance parameters representing a measuredperformance of the engine's HPC.

HPT RUL component 306 is configured to receive sensor data 302 as wellas a cumulative damage model (CDM) from CDM component 312. HPT RULcomponent 306 calculates one or more performance parameters including aremaining useful life (RUL) associated with the engine or a particularcomponent of the engine. In example embodiments, HPT RUL component 306can generate a useful life parameter for the turbine engine based on thesensor data and a cumulative damage model for the turbine engine.

Health state component 314 is configured to generate one or more healthstates for the turbine engine based on the outputs of the HPCperformance component 308, HPT RUL component 306, and compressorefficiency component 310. The health state component 314 generates ahealth state based on the measured condition or performance of theturbine engine as represented in the received performance parameters. Ahealth state generated by component 314 may include health stateinformation for the turbine engine, and/or health state information foran individual component of the turbine engine such as the compressor orturbine.

Wash interval component 316 receives health state information fromcomponent 314 and determines an optimal wash interval or timing of anext wash event for the turbine engine. The wash interval component cananalyze the health state information to determine an optimal timing tomaximize the hardware life of the turbine engine.

Wash type component 318 determines a wash type for the next wash eventbased on the health state information. A wash type may include a washduration, cleaning medium, and/or wash delivery technique. For example,the wash type component 318 may determine that a water wash should beused with a wand delivery mechanism in response to first health stateinformation and that a foam wash should be used with an internal washline delivery mechanism in response to second health state information.The first health state information may indicate a level of deteriorationthat is less that a level of deterioration indicated by the secondhealth state information.

Wash scope component 320 determines a wash scope for the next wash eventbased on the health state information. The wash scope may indicate athoroughness of the wash in one example. The wash scope may identifyparticular portions or components of the turbine engine to be washed.For example, the wash scope may identify particular borescope holes orinlet nozzles at which to apply a wash medium. The wash scope mayadditionally or alternatively identify particular portions of a washdelivery mechanism, such as particular wash lines, to be activated forthe discharge of the cleaning medium to the turbine engine.

HPT clean component 322 is configured to receive wash event informationfrom wash scope component 320 and trigger a wash of the HPT. In someembodiments, HPT clean component 322 generates an identifier for a washof the HPT. Similarly, HPC clean component is configured to receive washevent information from wash scope component 320 and trigger a wash ofthe HPC. In some embodiments, HPC clean component 324 generates anidentifier for a wash of the HPC. In one example, wash intervalcomponent 314, wash type component 318, wash scope component 320, HPCand HPT clean components 322 and 324 are subsystems of a wash decisioncomponent. The wash identifier component is configured to generate washidentifiers indicating wash intervals, types, scopes, and/or componentsfor washing.

Wash effectiveness component 326 receives information from the cleancomponents 322 and 324 and determines a level of effectiveness of a washevent. Component 326 generates an output that is used by CDM component312 to update the cumulative damage model of the engine or engine part.In example embodiments, component 326 may determine a projected measureof remaining useful life associated with the engine such that generatingthe wash identifier is based on the projected measure of remaininguseful life. In some examples, the projected performance recovery is ofa compression section of the turbine engine and the projected measure ofremaining useful life is of the compression section.

FIG. 3 is a flowchart describing a process 400 for optimizing washevents for a turbine engine. Process 400 may be performed to generate ahealth state based on engine condition and/or performance, and togenerate a wash identifier based on the health state. Process 400 may beperformed by a wash optimization system 300 in some embodiments,however, process 400 is not limited to such environments. Process 300may be implemented by a server or other computing device to determinehealth state information and generate wash identifiers such as a signalindicating a wash event. Process 400 may be performed by one or moredevices, such as one or more circuits or one or more specialized networkdevices configured to perform the described operations. Process 400 mayalternatively be implemented in whole or in part by a processor, asprocessor readable code for programming a processor for example. Process400 depicts a particular order of the described blocks for purposes ofillustration and discussion. Those of ordinary skill in the art, usingthe disclosures provided herein, will understand that various blocks ofany of the methods disclosed herein can be adapted, modified,rearranged, or modified in various ways without deviating from the scopeof the present disclosure.

At 402, the system receives sensor data representing measured engineparameters of a turbine engine. In some embodiments, 402 is performed byGPTF 304, HPT RUL component 306, and/or HPC performance component 308.In another example, block 402 is performed by data reduction component510 described hereinafter.

The sensor data can be obtained from one or more sensors 204 in oneexample. In another example, the sensor data can be obtained from adatabase or other storage location. The sensor data includes orrepresents measured parameters of the turbine engine. By way of example,the measured parameters may include operational data such as gas turbinetemperatures, pressures, fuel flow, rotor speeds, bleeds(all types),horsepower extraction, length of flight, and/or derived parameters basedon operational conditions. The measured parameters may additionally oralternately include environmental data such as ambient pressures,temperatures, altitudes, humidity, latitude, longitude, flight city,and/or particulates that include all types such as multiple dust/sandtypes, pollution/haze, sea salt, smoke, volcanic ash and/or sulfates.

At 404, the system determines at least one performance parameter basedon the sensor data. In some embodiments, block 404 is performed by HPTRUL component 306, HPC performance component 308, and/or compressorefficiency component 310. In another example, block 402 is performed bydata reduction component 510.

The performance parameters are calculated from the measured engineparameters in one example. For example, the performance parameters mayinclude a modular health indicator of one or more components of theengine or an accumulated condition of one or more component. Anaccumulated condition may be calculated based on environmental dataand/or derived damage factors based on models and/or analytics. By wayof example, performance parameters may include modular componentefficiencies such as HPT efficiency or HPC efficiency, air and fuel flowratess, and/or pressure ratios. Performance parameters may also includeresidual EGT, residual gas turbine temperature, or residual fuel flow toa particular engine component.

In example embodiments, performance parameters may include derivedparameters such as contamination levels determined based on sensor data.A contamination performance parameter may indicate a level ofcontaminants within an engine, engine component or module, or enginepart. A contamination performance parameter is a calculated amount ofparticulates that either block cooling passages or cause a thermal layerwhich impacts cooling air effectiveness in some embodiments.

Performance parameters are generated using one or more models in oneexample. A model can comprise a look-up table model or a physics-basedmodel (e.g., an aero-thermodynamic model) in one example. The modelcharacterizes the turbine engine by estimating outputs based on inputs.The model inputs can include sensor data such as actuator positions,temperatures, pressures, altitude, etc. and the outputs can includeprocessed sensor data including performance parameters such as pressureratios, temperature ratios, thrust, stall margins, etc. A model outputcan be calculated as an estimate for the sensor output based on a set ofoperating conditions or parameters of the turbine engine in the model.The system may utilize one or more contamination accumulation models todetermine a contamination performance parameter.

At 406, the system generates a health state for the turbine engine basedon the performance parameter(s). In some embodiments, block 406 isperformed by health state component 314. In another example, block 406is performed by data reduction component 510.

In some embodiments, the health state represents an accumulated healthcondition of the engine and/or a component of the engine. For example, ahealth state may be determined for the overall engine, and/or for one ormore components of the engine such as the compressor or turbine. In someembodiments, the health state is a value representing multipleperformance parameters associated with the engine or component. Thehealth state is an identifier of an engine's or a component's conditionand/or performance. The health state may represent an amount ofdegradation associated with the engine or component, or an amount ofoperational life remaining for the engine or component. In someembodiments, a health state is calculated based on a contaminationaccumulation model indicating particulate accumulations within theengine.

The system may also generate a reference state for the engine or one ormore engine components. The reference state may be based on accumulatedand/or normalized sensor data over time for one or more engines. Thereference state may represent an expected condition or performance ofone or more engine parameters. The reference state can simulate aturbine engine and be determined based on the model of the turbineengine representing engine parameters under different environmental andoperating conditions. The reference state provides reference informationincluding expected performance parameters of an engine based onparticular operational and/or environmental data. In some embodiments,block 406 includes comparing a current or projected health state with areference state.

At 408, the system generates a wash identifier based on the health stateof the engine or engine component. In some embodiments, block 408 isperformed by wash interval component 316, wash type component 318,and/or wash scope component 320. In another example, block 408 isperformed by wash decision component 522.

In some embodiments, a wash identifier is conditionally generated basedon the health state. A wash identifier may be generated based on thehealth state satisfying a threshold criterion. For example, the systemmay generate a wash identifier in response to determining that adifference between a health state and reference state satisfies athreshold criterion in one example, such as by the difference meeting orexceeding a threshold difference. In some embodiments, a wash identifieris generated in response to determining that a difference betweenindividual performance parameters associated with a reference state andhealth state satisfies a threshold criterion.

Various wash identifiers may be generated at block 408. In one example,the wash identifier is an indication that an engine wash should beperformed for a selected engine. The wash identifier may additionallyinclude a wash type identifier, a wash timing or interval identifier,and/or a wash scope identifier.

At 412, the system transmits a signal such as an output signal includingthe wash identifier. In one example, the signal indicates a notificationof a wash event. In some embodiments, the signal is transmitted to acomputing device associated with a technician. The signal generates anoutput indicating to a technician that a wash event should be performed.In another embodiment, the signal is transmitted to an automatedcleaning system. In response to the signal, the system automaticallyperforms the indicated wash event, including a wash type, timing, and/orscope.

FIG. 4 is a block diagram of a wash optimization system 500 inaccordance with example embodiments of the disclosed technology. Washoptimization system 500 provides additional details of wash optimizationsystem 300 in one example. In one example, system 500 is implementedusing one or more computing devices, such as one or more servers, clientdevices, and/or network devices. The one or more computing systems areone or more computing systems 600 in one example. Wash optimizationsystem 500 may be implemented in a control system aboard an aerialvehicle, such as by computing system 206, or can be implemented as aground-based system that receives sensor and/or other information froman aerial vehicle.

Wash optimization system 500 includes a contamination accumulationcomponent 508, data reduction component 510, health state comparisoncomponent 520, wash decision component 522, recovery assessmentcomponent 524, and modeling component 526. The components of system 500may be configured at a single computing device, or may be distributedacross multiple computing devices. Each of the various components may beimplemented with hardware, software, or a combination of both hardwareand software as hereinafter described.

Data reduction component 510 receives sensor data including measuredengine parameters such as environmental data 502 and/or operational data504. Data reduction component 510 processes the measured data anddetermines one or more performance parameters 506 associated with theengine. The data reduction component can make engine and modular healthstate calculations, including historical, current and predictiveparameter calculations. In additional to health state calculations, thereduction component may provide physics-based models that providethermodynamically balanced solutions to provide modular health states.

In some embodiments, the performance parameters represent a conditionand/or performance of the turbine engine. The performance parameters arederived from the measured engine parameters to represent actualcondition or performance of the engine. The performance parameters aregenerated by aggregating sensor data over time in some embodiments togenerate actual and/or expected performance parameters over the enginelife. Unlike measured engine parameters which indicate operational orenvironmental conditions alone, performance parameters indicate anactual performance or condition of the engine or component as a resultof the engine parameters. By way of specific example, the performanceparameters may include accumulated condition information, modularcomponent efficiencies, pressure ratios, and air and fuel flows.

Data reduction component 510 generates various health state informationbased on the performance parameters. In the example of FIG. 4, datareduction component 510 generates a reference state 512, compressionhealth state 514, turbine health state 516, and engine health state 518.In other examples, additional or fewer health states may be generated.Reference state 512 represents expected performance parameters of anengine or model type. The reference state 512 can be generated based onsensor data received over time. The system can track components andengines to develop expected performance parameter values. The sensordata is processed to determine the expected performance parameters.

Engine health state 518 represents an overall system health associatedwith the engine. Similarly, turbine health state 516 represents a healthof the turbine system. Compression health state 514 represents a healthof the compression system. In one example, a health state is generatedbased on the calculated performance parameters from the sensor data. Theperformance parameters associated with an engine or component can becombined to develop a health state that includes a value indicating ahealth of the engine or component in one example.

The data reduction component can track any part and provide remaininguseful life (RUL) or similar parameters. The component can runautomatically and continuously with the arrival of new sensor data.

Health state comparison component 520 compares an actual health stateassociated with an engine or component with a reference state for theengine or component. In some embodiments, comparison component 520compares individual performance parameters of an engine health state 518or component health state 514 or 516 with expected performanceparameters in a reference state 512. For example, a difference betweenactual performance parameters associated with an engine or component canbe compared with expected performance parameters. In another example,the system compares an overall health state value for the selectedengine or component with a reference state value for the selected engineor component.

Wash decision component 522 determines whether a wash event should beperformed based on the health state comparison results. For example, ifthe difference between actual engine or component health and expectedengine or component health satisfies a threshold criterion, a wash canbe scheduled and/or a wash identifier generated. For example, if thedifference exceeds a threshold difference, a wash identifier may begenerated.

Wash decision component 522 may determine a wash type, wash timing,and/or wash scope in association with a wash event. Decision component522 can generate a wash identifier that indicates the determined washtype, timing, and/or scope.

Recovery assessment component 524 determines an actual performancerecovery achieved by one or more wash events. The recovery assessmentcomponent may compare engine parameters prior to and after a wash eventto assess recovery. The assessment component may alternately oradditionally compare performance parameters determined from sensor data.In some embodiments, assessment component 524 determines a valuerepresenting an actual performance recovery associated with the engine.The assessment component 524 may compare the actual recovery with anexpected performance recovery of the wash event. This information may beprovided back to wash decision component 522 which may use theinformation to determine whether an additional wash should be performed.Additionally, an output of the assessment component may be used todetermine compressor performance parameters and/or turbine RULparameters.

Modeling component 526 is configured to reset or update various modelsbased on the recovery assessment. For example, predictive and/ordiagnostic models (e.g., the damage model generated by CDM component312) may be updated based on the recovery assessment.

According to some embodiments, the wash optimization system tracksengine health such as compression system health over time as part ofoptimizing the timing of washes. As part of health tracking in someembodiments, the system measures recovery associated with one or morewash events. For example, recovery assessment component 524 may measurean amount of recovery associated with one or more engine washes. Theamount of recovery may be used to generate a recovery parameter value insome embodiments.

In some embodiments, the system measures recoverable and/ornon-recoverable health of an engine or module. The assessment componentcan attribute recoverable and non-recoverable health. Recoverable healthis that associated with those environmental elements that deterioratethe engine health but can be removed with washing. Non-recoverablehealth includes those elements that are attributed to materialdegradation due to operation of a part. The system can measure and trackrecoverable health over time as part of modeling engine performance anddegradation.

In some embodiments, modeling component 526 tracks the long-term impactof washes and generates one or more models demonstrating the long-termimpacts of the washes. In example embodiments, these models can be usedby wash decision component 522 to determine an optimal wash timing, forexample. Additionally, these models can be used to determine optimalwash mediums, delivery methods, scopes, etc.

In some embodiments, modeling component 526 generates contaminationaccumulation models based on performance parameters and the measuredrecovery from wash events. The modeling component can determine andgenerate a model that demonstrates a level of particulates that areremoved during a wash event. The model may demonstrate the impacts onmultiple parts of the engine of removing the particulates.

In some embodiments, modeling component 526 integrates the performancerecovery and the removal of particulates into one or more models. Suchmodels indicate the impact of removing the particulates and the enginerecovery such as compression system recovery by a wash. The systemthereby indicates a level of improvement along with a projected level ofRUL for an engine, module, or part.

In accordance with example embodiments, a wash optimization systemintegrates contamination accumulation models into wash decisionprocesses. The system can be configured to calculate an amount ofparticulates that have accumulated within an engine, component, or partof an engine. The system may determine an amount of particulates thatblock cooling passages and/or that cause a thermal layer which impactscooling effectiveness. These particulate determinations may be used toschedule wash events to maximize hardware life.

Contamination accumulation component 508 can apply one or morecontamination accumulation models to determine particulate amountsassociated with the accumulation of contaminants within the engine.Component 508 may receive sensor data, such as environmental data, whichis used to estimate or predict one or more particulate amountsassociated with an engine or engine component based on one or moremodels. In example embodiments, the particulate amounts are used togenerate a compression health state 514 or a turbine health state 516.In some embodiments, data reduction component 510 is configured toassess a level of contaminants that have accumulated in an engine,module, or part. The level of contaminants may be used to generate oneor more contaminant parameter values. These parameter values may be usedto generate an engine or component health state as described at block406 in process 400.

FIG. 5 is a flowchart describing a process 420 of generating acontamination accumulation model in accordance with example embodiments.Process 420 may be performed by a wash optimization system 300 or 500 asshown in FIGS. 2 and 4, respectively. Process 420 may be implemented inor by one or more devices, such as one or more circuits or one or morespecialized network devices configured to generate a contaminationaccumulation model. Alternatively, the process may be implemented in aprocessor, as processor readable code for programming a processor forexample.

At 402, the system accesses or otherwise receives one or moreperformance parameters representing a contaminant accumulation in aturbine engine prior to a wash event. The performance parameters may bedetermined by contamination accumulation component 508 or data reductioncomponent 510 in FIG. 4. Sensor data may be processed to determine anamount of contaminants such as a particular accumulation, associatedwith an engine, engine component, or engine part.

At 404, the system receives one or more performance parametersrepresenting contaminant accumulation in the turbine engine after thewash event. The performance parameters at 404 can be determined asdescribed at block 402 following the wash event.

At 406, the system determines a recovery parameter value associated withthe engine wash event. For example, the system can compare one or moreparameters before and after the wash event to determine a recoveryvalue. The recovery value represents a level of contaminants that areremoved by the wash event.

At 408, the system updates or generates one or more contaminationaccumulation models to indicate the level of contaminant removalassociated with the wash. Block 408 may include adding or updating amodel to include the recovery parameter value. In some embodiments,block 908 includes aggregating the parameter value with existing valuesto indicate removal levels.

With reference to FIG. 3, the system in some embodiments may use acontamination accumulation model at step 404 to determine performanceparameters and/or at step 406 to generate a health state. As such, awash identifier may be generated at step 408 that is based on thecontamination accumulation model.

FIG. 6 is a flowchart describing a process 720 in accordance withexample embodiments of conditionally indicating that an engine washshould be performed based on a measured condition or performance of aturbine engine. Process 720 uses a threshold criterion to determinewhether a health state of the turbine engine indicates that an enginewash should be performed. Process 720 is one example of a process ofgenerating a wash identifier that can be performed at block 408 of FIG.3. Process 720 may be performed by a wash optimization system 300 or 500as shown in FIGS. 2 and 4, respectively. Process 720 may be implementedin or by one or more devices, such as one or more circuits or one ormore specialized network devices configured to generate wash identifiersbased on health state information. Alternatively, the process may beimplemented in a processor, as processor readable code for programming aprocessor for example.

At 724, the system compares health state information of an engine orengine component with reference state information corresponding to theengine or engine component. In some embodiments, block 724 is performedby health state component 314 in FIG. 2 or comparison unit 520 in FIG.4. In one example, block 724 includes comparing a health state valuerepresenting an accumulated health condition of the turbine engine witha reference state value. In another example, block 724 includescomparing individual performance parameter values of a health state withreference parameter values.

At 726, the system determines whether the health state satisfies athreshold criterion. In this example, the system determines whether adifference between the health state and the reference state exceeds athreshold difference. Other techniques for determining if a thresholdcriterion has been satisfied can be used.

If the difference does not exceed the threshold, the process returns atblock 728 without generating a wash identifier. The system determinesthat the threshold criteria for scheduling or performing a wash have notbeen met so no wash identifier is generated. In one example, block 728includes generating an identifier that a wash should not be performed.In another example, block 728 includes not generating any identifier.

If the difference exceeds the threshold, an optimal wash timing isdetermined at 730. In one example, the system calculates a long termimpact of washing and predicts a level of improvement associated withwashing. Based on tracking the engine health information over time, thesystem identifies an optimized timing for the wash. The optimal timingmay be based on increasing engine performance, as well as cost andefficiency considerations with removing the engine from use for the washevent.

At 732, the system determines a wash type for the wash. The wash typemay include a cleaning medium, a cleaning duration, and/or a mediumdelivery method or apparatus. The wash type can be determined based onthe health state of the engine or component to be cleaned.

At 734, the system determines a wash scope for the wash. The wash scopemay identify particular portions of an engine or component to becleaned, and/or particular components of a wash delivery apparatus to beused to deliver the cleaning medium. For example, the system maydetermine from the health state information particular portions of theengine with parameters below expectations. The system can select a washscope corresponding to the particular engine portions.

At 736, the system generates a wash identifier indicating the washtiming, wash type, and/or the wash scope.

FIG. 7 is a flowchart of a process 750 in accordance with exampleembodiments of determining a wash type and timing for a wash event.Process 750 is one example of a process of selecting a wash type andtiming and generating a wash identifier that can be performed at blocks730 and 732 of FIG. 6 in one example. Process 750 may be performed by awash optimization system 300 or 500 as shown in FIGS. 2 and 4,respectively. Process 750 may be implemented in or by one or moredevices, such as one or more circuits or one or more specialized networkdevices configured to generate wash identifiers based on health stateinformation. Alternatively, the process may be implemented in aprocessor, as processor readable code for programming a processor forexample.

At 752, the system accesses health state information. In someembodiments, the system access engine health state information 518 orcomponent health state information 514, 516, for example. In someembodiments, the system accesses health state comparison information ascan be generated at block 724 of process 720.

At 754, the system determines an expected effectiveness of a pluralityof engine wash types based on the health state information. In someembodiments, the system uses one or more models associated withdifferent wash types. The system can input the performance parameters orhealth information and receive an output representing an amountperformance recovery, etc. that can be achieved by the associated washtype. In some embodiments, the system receives a plurality of parametersrelated to engine performance and a plurality of parameters relating toa particular wash type. The wash parameters may identify wash date,time, wash device, number of wash cycles, cleaning medium, and/or otherattributes that describe the wash type. Specific values or ranges ofvalues may be used to each of the engine wash parameters. The system candetermine an effectiveness of the wash type based on the performanceparameters and the wash parameters. The system can generate engineparameters representing the engine performance after the wash event. Theafter wash engine parameters can be generated based on aggregated enginewash information, one or more models of engine wash effectiveness, etc.The system compares the received performance parameters with theperformance parameters simulated for the wash event and determines aneffectiveness of the wash type.

In some embodiments, the normalized performance parameters can becompared with the expected performance parameters. An appropriate washtype to place the engine parameters at or closer to the expectedperformance parameters can be selected. For example, if a water wash fora short duration delivered by wand will clean the engine sufficiently tomeet expected performance parameters, that particular wash type can beselected. If additional cleaning by foam, alcohol, or internal deliveryby wash lines, is needed, the corresponding wash type can be selected.

At 756 and 758, the system selects a wash type and wash timing,respectively, based on the expected effectiveness. In one example, thesystem selects the wash type having highest rated effectiveness. Inother examples, the system may select a wash type based having a currenthighest rated effectiveness. In another example, the system selects awash type having a highest projected level of effectiveness at aspecific time in the future. The wash timing can be selected to maximizethe life expectancy of the engine or component. The wash timing can alsobe selected to maximize efficiency of the maintenance process (e.g.,reduce costs) and avoid unnecessary washes.

At 760, the system generates an identifier of the selected wash type andwash timing.

FIG. 8 is a flowchart describing a process 780 in accordance withexample embodiments of determining a wash scope for a wash event.Process 780 is one example of a process of selecting a wash scope andgenerating a wash identifier that can be performed at block 734 of FIG.6 in one example. Process 780 may be performed by a wash optimizationsystem 300 or 500 as shown in FIGS. 2 and 4, respectively. Process 780may be implemented in or by one or more devices, such as one or morecircuits or one or more specialized network devices configured togenerate wash identifiers based on health state information.Alternatively, the process may be implemented in a processor, asprocessor readable code for programming a processor for example.

At 782, the system identifies an engine or engine component that isassociated with a wash event. At 784, the system determines if the washtype of the event specifies a wand wash to be applied externally to theturbine engine or a line wash to deliver the cleaning medium internally.

If the wash type is set to a wand wash, the system selects portions ofthe engine or component for wash at 794. In one example, the systemidentifies particular engine portions based on the health stateinformation, such as performance parameters relating to particularengine portions. At 796, the system generates one or more identifiers ofthe engine portion(s) to be targeted by the wand wash. In someembodiments, block 794 is omitted and the system generates an identifierto wash the engine or an engine component, without identifyingparticular portions thereof.

If the wash type is set to line wash, the system selects portions of theengine or engine component to be washed at 786. The portions areidentified based on health state information in one example.

At 788, the system identifies particular borescope holes, boosterinlets, and/or inlet nozzles and the like for application of thecleaning medium. The system may identify particular holes or nozzlesthrough which targeted portions of the engine or component can bereached.

At 790, the system identifies wash lines, valves, etc. of the cleaningapparatus that can be used to deliver the cleaning medium to theidentified borescope holes, inlet nozzles, etc. As hereinafterdescribed, a multiple wash line system may be used where spray nozzlesare attached to individual borescope holes, etc. Block 790 can includeidentifying particular lines, nozzles, etc. to attach to the identifiedborescope holes. Additionally, a wash system may include valves or adistribution manifold, etc. to control the flow of a cleaning medium toparticular wash lines. Block 790 may include identifying particularvalves or portions of the distribution manifold to control or vary thedistribution of flow of cleaning medium to different portions of thewash system and thereby different portions of the engine or component.Each valve may be independently operable to precisely control thedelivery of engine portions commensurate with the desired wash scope.

At 792, the system generates one or more identifier(s) of the particularportions of the delivery system.

FIGS. 9-12 depict a wash system in accordance with example embodimentsthat includes a controllable distribution of cleaning medium. FIG. 9provides a perspective view of wash module 24 as it may be containedwithin a modular foam cart (not shown)according to example embodimentsof the present subject matter. Wash module 24 is one example of a washsystem that includes multiple lines for delivery a cleaning mediuminternally to a turbine engine. FIG. 10 provides a schematic view of thewash module 24 in accordance with an example aspect of the presentdisclosure. The wash module 24 of FIGS. 9 and 10 may, in certain exampleembodiments, be utilized with the wash optimization systems 300 and 500described above with reference to FIGS. 2 and 4, respectively. However,it should be appreciated, that in other embodiments the wash module 24described with reference to FIGS. 9 and 10 may instead be utilized withany other suitable wash system, such as a single, integrated washsystem.

As illustrated, the wash module 24 generally includes a pump 54, anddistribution manifold 56, and a plurality of wash lines 58. Morespecifically the pump 54 is configured to receive a flow of wash liquidand pressurize the flow of wash liquid. The pump 54 is configured to bereleasably fluidly connected to an outlet 38 of a wash tank 36 of a washtank module 22. For example, for the embodiment depicted, the washmodule 24 includes a fluid connection line 60, with the fluid connectionline 60 configured to be releasably fluidly connected to an outlet 38 ofa wash tank 36 of a wash tank module 22. For example, when utilized withthe wash tank module 22, the fluid connection line of the wash module 24may be releasably fluidly connected to the outlet 38 through quickrelease connection 40.

Although not depicted, the pump 54 may include a variable frequencydrive motor, such that it may operate at various power levels. However,in other embodiments, any other suitable pump may be utilized, includingany other suitable type of motor (such as a constant frequency motor).Additionally, as shown, the pump 54 is electrically connected to a powersource 62, which may be a battery, or any other suitable power source.The power source 62 may provide the pump 54 with a necessary amount ofelectrical power to pressurize the wash liquid received to a desiredpressure.

An outlet 64 of the pump 54 is fluidly connected to a duct 66 extendingto the distribution manifold 56, such that the distribution manifold 56is fluidly connected to the pump 54 for receiving a flow of pressurizedwash liquid from the pump 54. For the embodiment depicted, upstream ofthe distribution manifold 56, the wash module 24 includes a sensor 68for, e.g., sensing a temperature and or pressure, and a valve 70. Thevalve 70, for the embodiment depicted, is positioned in the duct 66 andmovable between an open position allowing full flow of wash liquidthrough the duct 66 and a closed position, preventing any flow of washliquid through the duct 66. In certain example embodiments, the valve 70may be a variable throughput valve movable between various positionsbetween the open position and the closed position to allow a desiredamount of wash liquid through the duct 66.

Distribution manifold 56 is configured to receive a flow of wash liquidfrom the duct 66 (i.e., a flow of pressurized wash liquid from the pump54), and distribute such flow of wash liquid to the plurality of washlines 58. The distribution manifold 56 may be operably connected to acontroller 72 of the wash module 24. Notably, the controller 72 mayfurther be operably connected to various other components of the washmodule 24. Specifically, for the embodiment depicted, the controller 72is operably connected to the power source 62, the pump 54, the sensor68, and the valve 70, in addition to the distribution manifold 56. Thecontroller 72 may be configured similar to the computing device 32 ofthe control system 30, and may be in communication with the controlsystem 30 of the wash system 20 through, e.g., a wireless communicationnetwork 34. For example, as will be described in greater detail below,the controller 72 may be configured to control a flow of pressurizedwash liquid to the plurality of wash lines 58 through the distributionmanifold 56.

Moreover, as is depicted, the plurality of wash lines 58 are fluidlyconnected to the distribution manifold 56 for receiving at least aportion of the pressurized wash liquid therefrom. Although for theembodiment depicted, the distribution manifold 56 is fluidly connectedto four (4) wash lines 58, in other embodiments, the wash module 24 ofthe wash system 20 may instead include any other suitable number of washlines 58 fluidly connected to the distribution manifold 56. As will beappreciated from the description below, the distribution manifold 56 maybe configured, in certain embodiments, to distribute the flow ofpressurized wash liquid in a fixed manner. For example, the distributionmanifold 56 may be configured to split the flow of pressurized washliquid substantially evenly between each of the plurality of wash lines58 fluidly connected thereto. Additionally, or alternatively, thedistribution manifold 56 may be configured to split the flow ofpressurized wash liquid in an uneven manner between the plurality ofwash lines 58 fluidly connected thereto (i.e., distributing more washliquid to certain wash lines 58 than others). In still other exampleembodiments, the distribution manifold 56 may be configured to vary adistribution of the flow of the pressurized wash liquid between thevarious wash lines 58 according to, e.g., individual spray schedules forthe various wash lines 58.

For example, referring now to FIG. 11, a wash system, or moreparticularly, a wash module 24 including a distribution manifold 56, inaccordance with another example embodiment of the present disclosure isdepicted. As with the embodiment of FIG. 10, the example distributionmanifold 56 is fluidly connected to the pump 54 of the wash module 24via a duct 66. Additionally, as is discussed in greater detail below,the wash module 24 further includes a plurality of spray nozzles 74,with each spray nozzle 74 attached to a respective wash line 58. Each ofthe plurality of spray nozzles 74 includes an attachment portion 76 forattachment to a respective borescope hole in a gas turbine engine,providing a substantially air-tight and water-tight connection to aborescope hole (see, e.g., borescope hole 146 in FIG. 12).

Furthermore, the example distribution manifold 56 is configured to varya distribution of the flow of pressurized wash liquid between thevarious wash lines 58. Specifically, the distribution manifold 56includes a plurality of valves 78, with each of the plurality of valves78 fluidly connecting a respective wash line 58 to the pump 54. Each ofthe valves 78 may be a variable throughput valve movable between a fullyopen position allowing complete flow of pressurized wash liquidtherethrough, a fully closed position allowing no flow of pressurizedliquid therethrough, as well as a variety of positions therebetween. Forexample, one or more of the variable throughput valves 78 may beconfigured as solenoid valves, or solenoid activated valves, oralternatively as ratio regulation valves.

Moreover, for the embodiment depicted each of the plurality of valves 78is individually operably connected to the controller 72, such that theplurality of valves 78 are operable independently of one another.Accordingly, the controller 72 may control the plurality of valves 78such that each operates according to its own unique flow schedule (e.g.,flow rate, pressure, duration, etc.).

In addition to the plurality of valves 78, the distribution manifold 56further includes a plurality of flow meters 80, wherein each flow meter80 is in fluid communication with a wash line 58 of the plurality ofwash lines 58 to measure a flowrate of the pressurized wash liquidflowing therethrough. More specifically, for the embodiment depicted,the distribution manifold 56 includes a flow meter 80 downstream fromeach of the valves 78, for measuring a flowrate of wash liquid flowingto (and through) each wash line 58 . However, in other embodiments, oneor more of the flow meters 80 may instead be positioned upstream of arespective valve 78, or at any other suitable location.

As with the plurality of valves 78, each of the flow meters 80 isoperably connected to the controller 72, such that the controller 72 mayreceive information indicative of a flowrate of wash liquid through eachwash line 58 from the respective flow meters 80. The controller 72 mayutilize such information in controlling one or more of the plurality ofvalves 78. For example, the controller 72 may operate on a feedback loopto ensure wash liquid is flowing to and through a particular wash line58 at a desired flow rate.

Referring now to FIG. 12, a schematic view of a wash module 24 of a washsystem 20 in accordance with an example embodiment of the presentdisclosure is depicted, being utilized in washing operations of a gasturbine engine. In some embodiments, the wash module 24 of FIG. 12 maybe configured in substantially the same manner as example wash module 24described above. For example, the wash module 24 generally includes apump 54, a distribution manifold 56 fluidly connected to the pump 54 forreceiving a flow of pressurized wash fluid therefrom, and a plurality ofwash lines 58 fluidly connected to the distribution manifold 56.

As stated, the wash module 24 is being utilized in the embodimentdepicted in FIG. 12 in washing operations of a gas turbine engine, alsodepicted schematically. The gas turbine engine depicted is configured asa high bypass turbofan engine, referred to herein as “turbofan 100.” Asis depicted, the turbofan 100 defines an axial direction A (extendingparallel to a longitudinal centerline 101 provided for reference), aradial direction R, and a circumferential direction C (extending aboutthe axial direction A). Additionally, the turbofan 100 includes a fansection 102 and a turbine engine 104 disposed downstream from the fansection 102. The turbine engine 104 depicted generally includes asubstantially tubular outer casing 106 that defines an annular inlet108. The outer casing 106 encases, in serial flow relationship, acompressor section including a second, booster or low pressure (LP)compressor 110 and a first, high pressure (HP) compressor 112; acombustor section 114; a turbine section including a first, highpressure (HP) turbine 116 and a second, low pressure (LP) turbine 118;and a jet exhaust nozzle section 120. The compressor section, combustorsection 114, and turbine section together define a core air flowpath 121extending from the annular inlet 108 through the LP compressor 110, HPcompressor 112, combustor section 114, HP turbine 116 section 116, LPturbine section 118 and jet nozzle exhaust section 120. A first, highpressure (HP) shaft or spool 122 drivingly connects the HP turbine 116to the HP compressor 112. A second, low pressure (LP) shaft or spool 124drivingly connects the LP turbine 118 to the LP compressor 110.

For the example embodiment depicted, the fan section 102 includes a fan126 having a plurality of fan blades 128 coupled to a disk 130 in aspaced apart manner. As depicted, the fan blades 128 extend outwardlyfrom disk 130 generally along the radial direction R. In certainaspects, the fan 126 may be a variable pitch fan, such that each of theplurality of fan blades 128 are rotatable relative to the disk about apitch axis, by virtue of the plurality of fan blades being operativelycoupled to an actuation member.

Referring still to the example embodiment of FIG. 12, the disk 130 iscovered by rotatable front hub 136 aerodynamically contoured to promotean airflow through the plurality of fan blades 128. Additionally, thefan section 102 includes an annular fan casing or outer nacelle 138 thatcircumferentially surrounds the fan 126 and/or at least a portion of theturbine engine 104. The nacelle 138 is supported relative to the turbineengine 104 by a plurality of circumferentially-spaced outlet guide vanes140. A downstream section 142 of the nacelle 138 extends over an outerportion of the turbine engine 104 so as to define a bypass airflowpassage 144 therebetween.

Referring still to FIG. 12, the fan blades 128, disk 130, and front hub136 are together rotatable about the longitudinal axis 101 directly bythe LP spool 124. Accordingly, for the embodiment depicted, the turbofanengine 100 may be referred to as a “direct drive” turbofan engine.However, in other embodiments, the turbofan engine 100 may additionallyinclude a reduction gearbox for driving the fan 126 at a reducedrotational speed relative to the LP spool 124.

Throughout the turbofan engine 100, the turbine engine 104 defines aplurality of borescope holes 146. Specifically, for the embodimentdepicted, the turbine engine 104 includes one or more borescope holes146 defined in the compressor section, in the combustor section 114, andin the turbine section. More specifically, still, for the embodimentdepicted, the turbine engine 104 includes one or more borescope holes146 defined in the LP compressor 110, the HP compressor 112, acombustion chamber of the combustor section 114, the HP turbine 116, andthe LP turbine 118. The borescope holes 146 may allow for inspection ofthe turbine engine 104 between operations, and more specifically, mayopen into the core air flowpath 121 of the turbofan engine 100 to allowfor inspection of, e.g., one or more blades, nozzles, or combustionliners of the turbofan engine 100 between operations. By contrast,during normal operations, the borescope holes 146 within the combustorsection 114 and turbine section may be plugged with a borescope plug(not shown), such that the borescope holes 146 do not affect operationof the turbofan engine 100.

Moreover, as previously stated, the turbofan engine 100 is depictedschematically as being cleaned by the wash module 24 of the wash system20. More specifically, the wash module 24 of the wash system 20 furtherincludes a plurality of spray nozzles 74, each of the plurality of spraynozzles 74 attached to a respective wash line 58 and configured forextending at least partially into or through one of the borescope holes146 of the turbofan engine 100 for providing at least a portion of theflow of the pressurized wash liquid to the turbofan engine 100. Morespecifically, the plurality of spray nozzles 74 may provide at least aportion of the flow of pressurized wash liquid directly to the core airflowpath 121 of the turbine engine 104, at a location downstream fromthe inlet 108. It should be appreciated, that in certain embodiments,the plurality of spray nozzles 74 may extend at least partially into orthrough borescope holes 146 of the turbofan engine 100 at locationsspaced along, e.g., the circumferential direction C of the turbofanengine 100. Such a configuration may allow for a more even cleaning ofthe turbofan engine 100, or rather of the turbine engine 104, duringsuch wash operations.

Referring still to FIG. 12, for the embodiment depicted, the pluralityof spray nozzles 74 includes a compressor spray nozzle 74A for extendingat least partially into or through one of the borescope holes 146defined in the compressor section of the turbofan engine 100, as well asa turbine spray nozzle 74B for extending at least partially into orthrough one of the borescope holes 146 defined in the turbine section ofthe turbofan engine 100. Further, for the embodiment depicted, theplurality spray nozzles 74 includes a combustor section spray nozzle 74Cfor extending at least partially into or through one of the borescopeholes 146 defined in a combustion chamber of the combustor section 114of the gas turbine engine.

More specifically, for the embodiment depicted, the compressor spraynozzle 74A includes a plurality of compressor spray nozzles 74A (a firstplurality of spray nozzles 74 positioned within borescope holes 146 in afirst region of the turbofan engine 100), with at least one spray nozzle74A extending into or through a borescope hole 146 defined in the LPcompressor 110 and at least one spray nozzle 74A extending into orthrough a borescope hole 146 defined in the HP compressor 112. Further,for the embodiment depicted, the turbine spray nozzle 74B includes aplurality of turbine spray nozzles 74B (a second plurality of spraynozzles 74 positioned within borescope holes 146 in a second region ofthe turbofan engine 100), with at least one spray nozzle 74B extendinginto or through a borescope hole 146 defined in the HP turbine 116 andat least one spray nozzle 74B extending into or through a borescope hole146 defined in the LP turbine 118.

Additionally, the wash module 24 further includes an inlet nozzleassembly 82 fluidly connected to one or more of the plurality of washlines 58 for providing at least a portion of the flow of pressurizedwash liquid to the turbofan engine 100, or rather to the turbine engine104, through the inlet 108 of the turbine engine 104. As is depicted,the inlet nozzle assembly 82 includes one or more inlet nozzles 84positioned proximate the inlet 108 to the turbine engine 104 to spraywash liquid directly into and through the inlet 108 of the turbineengine 104. In other embodiments, however, the inlet nozzle assembly 82may instead be located at least partially forward of the fan 126.

Referring still to FIG. 12, as noted above, the turbofan engine 100includes the outer nacelle 138 which defines the bypass passage 144 withthe turbine engine 104. For the embodiment depicted, the plurality ofwash lines 58 extend from an aft end of the turbine engine 104, throughthe bypass passage 144 to each of the respective plurality of borescopeholes 146, and to the inlet 108 for the inlet nozzle assembly 82. Withsuch a configuration, the wash system 20 may operate without having toremove one or more portions of the fan section 102. More specifically, awash system having such a configuration may allow for conducting washingoperations (i.e., providing pressurized wash liquid through theplurality of wash lines and wash nozzles), while allowing for theturbofan engine to be cranked or rotated using, e.g., a starter motor orturning tool 172, to increase in effectiveness of the washingoperations. In addition, a controller, such as a system controller orcontroller 52, can automatically control the speed of the engine corerotation to improve cleaning performance or to prevent unwanted washfluid intrusion into internal engine air circuits or other passageways.Moreover, such a controller can be configured for monitoring motortorque, e.g., to protect gearbox components.

Utilizing a wash system in accordance with one or more of the exampleembodiments described herein may allow for efficient cleaning of the gasturbine engine. More specifically, by providing a wash liquid directlyto a core air flowpath of the turbine engine of the gas turbine enginemay allow the wash system to provide such portions with heated andpressurized wash liquid. By contrast to prior configurations, in whichwash liquid is provided solely at an inlet to the turbine engine (inwhich case such wash liquid may be neither pressurized nor heated by thetime it reaches e.g., a turbine section), providing wash liquid directlyto e.g., a turbine section of the turbine engine may allow the washsystem to provide heated and pressurized wash liquid to such section.Additionally, embodiments including the individual valves fluidlyconnecting wash lines to a pump in a distribution manifold may allow forrelatively precise cleaning of the gas turbine engine and/or targetedcleaning of a gas turbine engine. Moreover, the duration of the cleaningcycle may be adjusted, the density of the cleansing foam may be altered,and other adjustments to the cleaning cycle may be adjusted to improvecleaning efficiency.

FIG. 13 depicts a wash system 802 in accordance with example embodimentsthat includes a wand for delivering a cleaning medium. Wash system 802is one example of a wash system that includes a wand for delivery of acleaning medium externally to a turbine engine.

In FIG. 13, a simplified diagram is depicted illustrating a water-washoperation performed on jet engine 812. In this example, a water-washsystem or wand 802 comprises a set of water injection nozzles 804 thatinject streams of water or a cleaning solution 806 into the inlet ofcompressor 824, thereby removing accumulated contaminants from thecompressor 824 and restoring performance levels. In this example, wand802 includes four injection nozzles, but it will be appreciated that awand may have any number of injection nozzles. Additionally, wand 802may include additional structural components not shown. For example,wand 802 may include a handle to enable user hand-held operation or amember for attaching the wand to a vehicle or other portion of a washsystem.

A water-wash operation can be carried out as an off-line process wherebythe water-wash is applied while the jet engine is not operating (e.g.,as a ground-based operation). In such scenarios, the water-wash system802 may be a separate system may be a separate machine that can bedirected to the inlet of the compressor 824 while the jet engine isgrounded. Alternatively, a water-wash operation may be carried out as anon-line (on-wing) process whereby an integrated water-wash injection isdirected to the inlet of the compressor 824 during operation of the jetengine 812

FIG. 14 depicts a block diagram of an example computing system 600 thatcan be used by an aerial vehicle, a ground system, or other systems ofthe aerial vehicle to implement methods and systems according to exampleembodiments of the present disclosure. As shown, the computing system600 can include one or more computing device(s) 602. The one or morecomputing device(s) 602 can include one or more processor(s) 604 and oneor more memory device(s) 606. The one or more processor(s) 604 caninclude any suitable processing device, such as a microprocessor,microcontroller, integrated circuit, logic device, or other suitableprocessing device. The one or more memory device(s) 606 can include oneor more computer-readable media, including, but not limited to,non-transitory computer-readable media, RAM, ROM, hard drives, flashdrives, or other memory devices.

The one or more memory device(s) 606 can store information accessible bythe one or more processor(s) 604, including computer-readableinstructions 608 that can be executed by the one or more processor(s)604. The instructions 608 can be any set of instructions that whenexecuted by the one or more processor(s) 604, cause the one or moreprocessor(s) 604 to perform operations. The instructions 608 can besoftware written in any suitable programming language or can beimplemented in hardware. In some embodiments, the instructions 608 canbe executed by the one or more processor(s) 604 to cause the one or moreprocessor(s) 604 to perform operations, such as the operations forselecting an engine wash type, as described with reference to FIG. 2and/or FIG. 5, and/or any other operations or functions of the one ormore computing device(s) 602.

The memory device(s) 606 can further store data 610 that can be accessedby the processors 604. For example, the data 610 can include one or moreparameters related to engine performance, engine health data,operational data, derate history, environmental data, engine cycleinformation, etc., as described herein. The data 610 can include one ormore table(s), function(s), algorithm(s), model(s), equation(s), etc.for selecting an engine wash type according to example embodiments ofthe present disclosure.

The one or more computing device(s) 602 can also include a communicationinterface 612 used to communicate, for example, with the othercomponents of system. The communication interface 612 can include anysuitable components for interfacing with one or more network(s),including for example, transmitters, receivers, ports, controllers,antennas, or other suitable components.

The technology discussed herein makes reference to computer-basedsystems and actions taken by and information sent to and fromcomputer-based systems. One of ordinary skill in the art will recognizethat the inherent flexibility of computer-based systems allows for agreat variety of possible configurations, combinations, and divisions oftasks and functionality between and among components. For instance,processes discussed herein can be implemented using a single computingdevice or multiple computing devices working in combination. Databases,memory, instructions, and applications can be implemented on a singlesystem or distributed across multiple systems. Distributed componentscan operate sequentially or in parallel.

Although specific features of various embodiments may be shown in somedrawings and not in others, this is for convenience only. In accordancewith the principles of the present disclosure, any feature of a drawingmay be referenced and/or claimed in combination with any feature of anyother drawing.

This written description uses examples to disclose the claimed subjectmatter, including the best mode, and also to enable any person skilledin the art to practice the claimed subject matter, including making andusing any devices or systems and performing any incorporated methods.The patentable scope of the disclosed technology is defined by theclaims, and may include other examples that occur to those skilled inthe art. Such other examples are intended to be within the scope of theclaims if they include structural elements that do not differ from theliteral language of the claims, or if they include equivalent structuralelements with insubstantial differences from the literal languages ofthe claims.

1-20. (canceled)
 21. A method comprising: generating, by a systemcomprising at least one processor, a health state of an engine or anengine component; determining, by the system, a difference between thehealth state of the engine or the engine component and a reference statecorresponding to the engine or the engine component; determining, by thesystem, that the difference between the health state and the referencestate exceeds a threshold difference; and generating, by the system, awash identifier.
 22. The method of claim 21, wherein the health state ofthe engine or the engine component comprises at least one of: anaccumulated health condition of the engine, and an individualperformance parameter value of the engine or the engine component. 23.The method of claim 21, wherein the reference state comprises sensordata indicative of an expected condition or a performance of one or moreengine parameters.
 24. The method of claim 21, wherein generating, bythe system, the wash identifier comprises determining, by the system, atleast one of: a wash timing, a wash type, and a wash scope.
 25. Themethod of claim 24, wherein the wash timing comprises a timing for, orinterval during which to perform, an engine wash; wherein the wash typecomprises at least one of: a wash medium, a wash duration, a washdelivery method, and a wash delivery system; or wherein the wash scopecomprises at least one of: one or more portions or components of theengine to be washed, and one or more wash lines to be activated.
 26. Themethod of claim 21, comprising: generating the health state of theengine or the engine component based at least in part on one or moreparticulate values and a contamination accumulation model that predictsparticulate accumulation within the engine.
 27. The method of claim 26,comprising: receiving environmental data and using the environmentaldata in the contamination accumulation model to predict the particulateaccumulation within the engine.
 28. The method of claim 21, comprising:determining, by the system, an expected effectiveness of a plurality ofengine wash types based at least in part on the health state; andgenerating the wash identifier based at least in part on the expectedeffectiveness of the plurality of engine wash types, wherein the washidentifier comprises a wash type selected from among the plurality ofengine wash types.
 29. The method of claim 28, wherein the wash typeselected from among the plurality of engine wash types has a highestexpected effectiveness.
 30. The method of claim 29, wherein the highestexpected effectiveness comprises at least one of: a current highestrated effectiveness, and a highest projected level of effectiveness at aspecific time in the future.
 31. The method of claim 21, wherein thewash identifier comprise a wash type, and wherein the wash typecomprises at least one of: a wand wash to be applied externally, and aline wash to deliver a cleaning medium internally.
 32. A computingdevice, comprising: one or more storage devices comprising processorreadable code; and one or more processors in communication with the oneor more storage devices, the one or more processors configured toexecute the processor readable code, wherein the processor readablecode, when executed by the one or more processors, causes the one ormore processors to: generate a health state of an engine or an enginecomponent; determine a difference between the health state of the engineor the engine component and a reference state corresponding to theengine or the engine component; determine whether the difference betweenthe health state and the reference state exceeds a threshold difference;and generate a wash identifier when the difference between the healthstate and the reference state exceeds the threshold difference.
 33. Thecomputing device of claim 32, wherein the health state of the engine orthe engine component comprises at least one of: an accumulated healthcondition of the engine and an individual performance parameter value ofthe engine or the engine component.
 34. The computing device of claim32, wherein the reference state comprises sensor data indicative of anexpected condition or a performance of one or more engine parameters.35. The computing device of claim 32, wherein the wash identifiercomprises at least one of: a wash timing, a wash type, and a wash scope.36. The computing device of claim 35, wherein the wash timing comprisesa timing for, or interval during which to perform, an engine wash;wherein the wash type comprises at least one of: a wash medium, a washduration, a wash delivery method, and a wash delivery system; or whereinthe wash scope comprises at least one of: one or more portions orcomponents of the engine to be washed, and one or more r wash lines tobe activated.
 37. The computing device of claim 32, wherein theprocessor readable code, when executed by the one or more processors,causes the one or more processors to: generate the health state of theengine or the engine component based at least in part on one or moreparticulate values and a contamination accumulation model that predictsparticulate accumulation within the engine.
 38. The computing device ofclaim 32, wherein the processor readable code, when executed by the oneor more processors, causes the one or more processors to: generate anidentifier that a wash should not be performed when the differencebetween the health state and the reference state does not exceed thethreshold difference.
 39. The computing device of claim 32, wherein theprocessor readable code, when executed by the one or more processors,causes the one or more processors to: determine an expectedeffectiveness of a plurality of engine wash types based at least in parton the health state; and generate the wash identifier based at least inpart on the expected effectiveness of the plurality of engine washtypes, wherein the wash identifier comprises a wash type selected fromamong the plurality of engine wash types.
 40. A wash system for aturbine engine, the wash system comprising: one or more sensorsconfigured to provide sensor data pertaining to one or more measuredengine parameters of a turbine engine; a computing system comprising anon-transitory computer-readable medium and one or more processors; anda communication bus communicatively coupling the one or more sensors andthe computing system; wherein the computer-readable medium comprisescomputer-executable instructions, which when executed by the one or moreprocessors, causes the one or more processors to perform a methodcomprising: determining, based at least in part on the sensor data,health state information pertaining to the turbine engine or to acomponent of the turbine engine, wherein the health state informationcomprises at least one of: a difference between a health state and areference state, and a comparison of the difference between the healthstate and the reference state to a threshold difference; determining anexpected effectiveness of a plurality of engine wash types based atleast in part on the health state information; and generating a washidentifier based at least in part on the expected effectiveness of theplurality of engine wash types, wherein the wash identifier comprises awash type selected from among the plurality of engine wash types.